Predicting Future Demand Analysis in the Logistics Sector Using Machine Learning Methods

Abstract

In this study, the potential of machine learning methods for analyzing future needs in the logistics sector was investigated. The research is conducted using the MATLAB platform. Numeric pallet demand data obtained from a logistics company are employed to train MLP, LSTM, and CNN models. Data security and confidentiality take priority during the data collection process. This dataset, comprising a total of 3,062 daily records, serves as the primary data source for the study. In the data preprocessing phase, missing or erroneous data is rectified, and outliers are detected and corrected. The models are tested to predict pallet quantities over periods of 25 days and 4 weeks. The results are evaluated by comparing the model predictions with actual data. Model performances are assessed using metrics such as MSE, RMSE, NRMSE, MAE, ESD, and RC. The outcomes of the last 25 days demonstrate that the LSTM model exhibits the lowest MSE (6,410.5571) and RMSE (80.0660) values. For the MLP model, the MSE value is calculated as 20,536.5564, and the RMSE value is 143.3058. Performance evaluations for the CNN model yield an MSE of 8,492.4297 and an RMSE of 92.1544. Furthermore, it is observed that the MLP model provides the best results for the 4-week forecasts. The results of this study indicate the success of the models used for predicting pallet transportation quantities in the logistics sector. In addition to this study, a contribution is made toward enabling logistics companies to make more informed and strategic decisions.